DAPSO and PSO-VAF in Linear Phase Digital Low Pass FIR Filter Design

Mudit Shukla,G R Mishra

doi:10.4236/cs.2014.53008

Abstract

Digital filters play a key role in the field of digital signal processing. This paper presents a linear phase digital low pass finite impulse response (FIR) filter design using particle swarm optimization and its two new variants, dynamic and adjustable particle swarm optimization (DAPSO) and particle swarm optimization with variable acceleration factor (PSO-VAF) and illustrates the superiority of the PSO-VAF method over PSO based methods. Two fitness functions are considered. The fitness1 is used to find the possible minimum ripples in pass band and stop band in case of PSO, DAPSO and PSO-VAF. Fitness2 is able to control the ripples in both bands separately. A comparison of simulation results demonstrates the performance of PSO and its methods in designing digital low pass FIR filters.

Highlights

In the modern age, digital signal processing (DSP) is the indispensable part of the human life, due to its numerous applications such as telecommunication, speech processing, consumer electronics systems, biomedical systems, image processing, military and defense electronics systems, aerospace and automotive electronics systems and industrial applications [1]
The tabu search was applied to design approximation problem of finite impulse response (FIR) digital filter with quantized coefficients using a flexible realization of the filter taps, which allows getting higher accuracy [6]
We propose two new variants of particle swarm optimization (PSO) for designing linear phase low pass FIR (LP FIR) filter

Summary

Introduction

Digital signal processing (DSP) is the indispensable part of the human life, due to its numerous applications such as telecommunication, speech processing, consumer electronics systems, biomedical systems, image processing, military and defense electronics systems, aerospace and automotive electronics systems and industrial applications [1]. The simplest and most popular way to design FIR filter is by windowing In this method, ideal impulse response is multiplied with different window functions, such as Butterworth, Chebyshev, Kaiser and Hamming etc., depending on the requirements of ripples on the pass and stop band, stop band attenuation and transition width. Parks-McClellan (PM) method based on Remez Exchange algorithm provides an optimum equiripple approximation to the desired frequency response In this method, the relative values of the amplitude error in the frequency bands are specified by weighting function and not by deviations themselves [4]. The tabu search was applied to design approximation problem of FIR digital filter with quantized coefficients using a flexible realization of the filter taps, which allows getting higher accuracy [6] Another population based algorithm, differential evolution (DE), was applied to design with different order FIR filter. We propose two new variants of PSO for designing linear phase low pass FIR (LP FIR) filter

FIR Filter Design Issues

Intelligent Optimization Techniques

Design Examples and Discussion

Conclusion